Determining Valine Side-Chain Rotamer Conformations in Proteins from Methyl <sup>13</sup>C Chemical Shifts: Application to the 360 kDa Half-Proteasome
Bibliographic record
Abstract
A method is presented for determining Val side-chain χ(1) rotamer distributions in proteins based exclusively on measured (13)C(γ1) and (13)C(γ2) chemical shifts. The approach selects an ensemble of 20 χ(1) values, calculates average methyl (13)C(γ1,γ2) chemical shifts via theoretical quantum chemical calculations and maximizes the agreement with the experimentally measured shifts using a genetic algorithm. The methodology is validated with an application involving six proteins for which (13)C(γ) chemical shifts and three-bond methyl-backbone scalar couplings are available. The utility of the methodology is demonstrated with an application to the 360 kDa 'half-proteasome' where the χ(1) rotameric distributions of Val residues are calculated on the basis of chemical shifts. For the most part the χ(1) profiles so obtained compare very well with those generated from the high-resolution (2.3 Å) X-ray structure of the proteasome. Both NMR and X-ray distributions are cross-validated by comparing calculated (1)H-(13)C methyl residual dipolar couplings with measured values, and the level of agreement is at least as good for the NMR derived χ(1) values. Notably, as the resolution of the X-ray data improves (rotamer distributions from 3.4 and 2.3 Å X-ray structures are compared with the NMR data), the agreement with the NMR gets significantly better. This emphasizes the importance of NMR approaches for the study of high molecular weight complexes that can be recalcitrant to high resolution X-ray analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".